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Matched-Filter Earthquake Detection Applied to City-Scale DAS Fibre-Optic Systems in Aotearoa New Zealand: What More Can We Detect?

Description: 

Matched-filters have been successfully applied to detect earthquakes recorded using Distributed Acoustic Sensing (DAS) systems in multiple settings including to detect geothermal seismicity and aftershocks. This previous work has demonstrated that matched-filters applied to DAS systems can be used to detect additional seismicity missed by standard cataloguing methods applied to standard sensor networks. Similarly, matched-filters and recently developed machine-learning enhanced workflows have been shown to provide enhanced detection performance on seismic recordings made using standard seismometers. Thus far, little work has been done to ameliorate these methods and datasets to provide optimal earthquake detection datasets and workflows.

In this work we test the sensitivity of matched-filters applied to two city-scale DAS arrays in Aotearoa New Zealand to reliably detect small aftershocks from a M 5.9 earthquake above the Hikurangi subduction zone. We also compare the sensitivity of the DAS arrays to multiple standard and state-of-the art earthquake detection methods applied to standard seismometer data. Using this case study, we provide a logical comparison between advanced methods applied to high-quality data and relatively simple matched-filter methods using high-fold data from DAS arrays to evaluate the relative benefits of each approach. We also explore a combined approach using DAS data alongside traditional seismic data to attempt to achieve an optimal detection solution.

Session: Advancing Seismology with Distributed Fiber Optic Sensing [Poster Session]

Type: Poster

Date: 5/3/2024

Presentation Time: 08:00 AM (local time)

Presenting Author: Calum

Student Presenter: No

Invited Presentation: 

Authors